Free course · by Debugshala, Indore
Prompting and Context Tutorial
5 free lessons, in the order we teach them in class: start at lesson 1 and work down. Every lesson has an analogy, a diagram, code you can run or copy, a quick quiz and an FAQ.
- 5Lessons
- 1Modules
- ~1 hReading time
- FreeAlways
01Prompt and Context Engineering
- Prompt Engineering Guide: Write LLM Prompts That Work ReliablyA practical prompt engineering guide: roles, clear instructions, few-shot examples, JSON output, step-by-step caveats and Python prompt templates you can run.
- Prompting Techniques for Reasoning: CoT, Self-Consistency, ToTLearn reasoning prompting techniques: chain-of-thought, self-consistency voting and Tree of Thoughts, with runnable Python simulations and cost trade-offs.
- Structured Output in LLMs: JSON Prompting and Schema GuideGet reliable JSON from LLMs: JSON mode vs schema-constrained output, schema design rules, Pydantic with the OpenAI SDK, and a validate-and-retry loop.
- Context Engineering Explained: What Goes Into an LLM's ContextContext engineering explained: the six kinds of context, the write, select, compress and isolate moves, token budgets and chat history compression in Python.
- Prompt Injection Explained: Attacks and How to Defend LLM AppsUnderstand direct and indirect prompt injection, why keyword filters fail, and layered defences: least privilege, taint tracking, approvals and link filtering.